The NumPy linear algebra functions rely on BLAS and LAPACK to provide efficient low level implementations of standard linear algebra algorithms. Those libraries may be provided by NumPy itself using C versions of a subset of their reference implementations but, when possible, highly optimized libraries that take advantage of specialized processor functionality are preferred. Examples of such libraries are OpenBLAS, MKL (TM), and ATLAS. Because those libraries are multithreaded and processor dependent, environmental variables and external packages such as threadpoolctl may be needed to control the number of threads or specify the processor architecture.
- OpenBLAS: https://www.openblas.net/
- threadpoolctl: https://github.com/joblib/threadpoolctl
Please note that the most-used linear algebra functions in NumPy are present in the main numpy namespace rather than in numpy.linalg. There are: dot, vdot, inner, outer, matmul, tensordot, einsum, einsum_path and kron.
Functions present in numpy.linalg are listed below.
Matrix and vector products
multi_dot matrix_power
Decompositions
cholesky qr svd
Matrix eigenvalues
eig eigh eigvals eigvalsh
Norms and other numbers
norm cond det matrix_rank slogdet
Solving equations and inverting matrices
solve tensorsolve lstsq inv pinv tensorinv
| Raises | |
LinAlgError | |